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Updated: Jan 13, 2026

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Inundation2Depth: A multi-source dataset for floodwater depth estimation in urban areas.

Jeffrey Blay1, Yared Gebregziabher2, Manoj K Jha3

  • 1Department of Built Environment, North Carolina A&T State University, 1601 E Market St., Greensboro, NC 27401, USA.

Data in Brief
|January 6, 2026
PubMed
Summary

A new dataset, Inundation2Depth, provides crucial flood depth data for urban areas, enabling better risk assessment and deep learning models for flood management. This resource supports GeoAI research on flood severity.

Keywords:
Deep learningGeospatial engineeringHydrodynamic modellingMachine learningNatural disasters, Risk modellingRemote sensing

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Area of Science:

  • Geospatial Artificial Intelligence (GeoAI)
  • Remote Sensing
  • Hydrology

Background:

  • Urban floods pose significant risks, but operational mapping often lacks critical flood depth data.
  • Developing accurate flood depth models is hindered by the scarcity of large, georeferenced, and well-labeled datasets.
  • Deep learning offers advanced solutions but requires substantial, high-quality training data.

Purpose of the Study:

  • To introduce the Inundation2Depth dataset, a novel resource for flood depth estimation.
  • To facilitate the development and evaluation of GeoAI models for urban flood severity assessment.
  • To lower data barriers and promote comparability in flood-related GeoAI research.

Main Methods:

  • Paired inundation extent and depth labels derived from aerial imagery and LiDAR (Light Detection and Ranging)-based Digital Terrain Models (DTMs).
  • Data collected from 12 flood-affected areas in North and South Carolina (2016-2018) covering diverse environmental conditions.
  • Multi-sensor remote sensing data (Optical and LiDAR) preprocessed for spatial consistency and provided as raster tiles for machine learning integration.

Main Results:

  • The Inundation2Depth dataset comprises 5925 overlapping tiles across 24,649.88 acres.
  • Data includes raw and normalized versions, suitable for direct integration into machine/deep learning pipelines.
  • Dataset validation performed using hydrodynamic modeling with the HEC-RAS Rain-on-Grid tool.

Conclusions:

  • The Inundation2Depth dataset addresses the critical need for georeferenced flood depth data.
  • Its standardized format and spatial diversity enhance its value for flood detection, segmentation, and damage assessment models.
  • This resource is expected to advance GeoAI research in urban flood severity and improve flood risk management strategies.